Let’s define some vectors which can be used for demonstrations:
manyNumbers <- sample( 1:1000, 20 )
manyNumbers
[1] 54 49 578 113 919 489 915 367 344 180 63 270 900 281 553 320 778 358 717 331
manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA
[1] 331 180 49 915 578 919 900 717 113 358 NA 344 367 778 553 320 63 489 270 NA NA 54 281
duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers
[1] 2 3 1 3 1 4 3 2 5 4
letters
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"
LETTERS
[1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"
mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters
[1] "t" "e" "j" "p" "v" "L" "C" "U" "X" "D"
manyNumbersWithNA instead of manyNumbers.all( manyNumbers <= 1000 )
[1] TRUE
all( manyNumbers <= 500 )
[1] FALSE
any( manyNumbers > 1000 )
[1] FALSE
any( manyNumbers > 500 )
[1] TRUE
all( !is.na( manyNumbers ) )
[1] TRUE
any( is.na( manyNumbers ) )
[1] FALSE
Input: logical vector Output: vector of numbers (positions)
which( manyNumbers > 900 )
[1] 5 7
which( manyNumbersWithNA > 900 )
[1] 4 6
which( is.na( manyNumbersWithNA ) )
[1] 11 20 21
manyNumbers[ manyNumbers > 900 ] # indexing by logical vector
[1] 919 915
manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions
[1] 919 915
somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]
[1] 919 915
"A" %in% LETTERS
[1] TRUE
c( "X", "Y", "Z" ) %in% LETTERS
[1] TRUE TRUE TRUE
all( c( "X", "Y", "Z" ) %in% LETTERS )
[1] TRUE
all( mixedLetters %in% LETTERS )
[1] FALSE
any( mixedLetters %in% LETTERS )
[1] TRUE
mixedLetters[ mixedLetters %in% LETTERS ]
[1] "L" "C" "U" "X" "D"
mixedLetters[ !( mixedLetters %in% LETTERS ) ]
[1] "t" "e" "j" "p" "v"
manyNumbers %in% 300:600
[1] FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
[18] TRUE FALSE TRUE
which( manyNumbers %in% 300:600 )
[1] 3 6 8 9 15 16 18 20
sum( manyNumbers %in% 300:600 )
[1] 8
NAsif_else( manyNumbersWithNA >= 500, "large", "small" )
[1] "small" "small" "small" "large" "large" "large" "large" "large" "small" "small" NA "small" "small"
[14] "large" "large" "small" "small" "small" "small" NA NA "small" "small"
if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )
[1] "small" "small" "small" "large" "large" "large" "large" "large" "small" "small"
[11] "UNKNOWN" "small" "small" "large" "large" "small" "small" "small" "small" "UNKNOWN"
[21] "UNKNOWN" "small" "small"
# here integer 0L is needed instead of real 0.0
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L )
[1] 0 0 0 915 578 919 900 717 0 0 NA 0 0 778 553 0 0 0 0 NA NA 0 0
unique( duplicatedNumbers )
[1] 2 3 1 4 5
unique( c( NA, duplicatedNumbers, NA ) )
[1] NA 2 3 1 4 5
duplicated( duplicatedNumbers )
[1] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
which.max( manyNumbersWithNA )
[1] 6
manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]
[1] 919
which.min( manyNumbersWithNA )
[1] 3
manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]
[1] 49
range( manyNumbersWithNA, na.rm = TRUE )
[1] 49 919
manyNumbersWithNA
[1] 331 180 49 915 578 919 900 717 113 358 NA 344 367 778 553 320 63 489 270 NA NA 54 281
sort( manyNumbersWithNA )
[1] 49 54 63 113 180 270 281 320 331 344 358 367 489 553 578 717 778 900 915 919
sort( manyNumbersWithNA, na.last = TRUE )
[1] 49 54 63 113 180 270 281 320 331 344 358 367 489 553 578 717 778 900 915 919 NA NA NA
sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )
[1] 919 915 900 778 717 578 553 489 367 358 344 331 320 281 270 180 113 63 54 49 NA NA NA
manyNumbersWithNA[1:5]
[1] 331 180 49 915 578
order( manyNumbersWithNA[1:5] )
[1] 3 2 1 5 4
rank( manyNumbersWithNA[1:5] )
[1] 3 2 1 5 4
sort( mixedLetters )
[1] "C" "D" "e" "j" "L" "p" "t" "U" "v" "X"
manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )
[1] 1.0 7.0 3.5 3.5 7.0 3.5 9.5 7.0 3.5 9.5
rank( manyDuplicates, ties.method = "min" )
[1] 1 6 2 2 6 2 9 6 2 9
rank( manyDuplicates, ties.method = "random" )
[1] 1 7 5 4 6 2 10 8 3 9
v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v
[1] -1.00000000 -0.50000000 0.00000000 0.50000000 1.00000000 -0.67396428 -0.13518803 -0.76440720
[9] -0.96543267 -0.36329583 -0.98591783 0.44122914 0.06678965 -0.15158257 -0.84128778
round( v, 0 )
[1] -1 0 0 0 1 -1 0 -1 -1 0 -1 0 0 0 -1
round( v, 1 )
[1] -1.0 -0.5 0.0 0.5 1.0 -0.7 -0.1 -0.8 -1.0 -0.4 -1.0 0.4 0.1 -0.2 -0.8
round( v, 2 )
[1] -1.00 -0.50 0.00 0.50 1.00 -0.67 -0.14 -0.76 -0.97 -0.36 -0.99 0.44 0.07 -0.15 -0.84
floor( v )
[1] -1 -1 0 0 1 -1 -1 -1 -1 -1 -1 0 0 -1 -1
ceiling( v )
[1] -1 0 0 1 1 0 0 0 0 0 0 1 1 0 0
heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights
Amy Eve Bob
166 170 177
names( heights )
[1] "Amy" "Eve" "Bob"
names( heights ) <- c( "AMY", "EVE", "BOB" )
heights
AMY EVE BOB
166 170 177
heights[[ "EVE" ]]
[1] 170
expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )
# A tibble: 8 x 2
x y
<int> <chr>
1 1 a
2 1 b
3 2 a
4 2 b
5 3 a
6 3 b
7 NA a
8 NA b
combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "b" "b" "b" "c" "c" "d"
[2,] "b" "c" "d" "e" "c" "d" "e" "d" "e" "e"
combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "a" "a" "b" "b" "b" "c"
[2,] "b" "b" "b" "c" "c" "d" "c" "c" "d" "d"
[3,] "c" "d" "e" "d" "e" "e" "d" "e" "e" "e"
Copyright © 2021 Biomedical Data Sciences (BDS) | LUMC